speech_recognition/FunASR
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This commit is contained in:
shixian.shi 2023-06-27 19:48:51 +08:00
parent 25de54910e
commit 8d519b9dc6
4 changed files with 16 additions and 7 deletions
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14
funasr/bin/asr_infer.py
View File

@ -285,6 +285,7 @@ class Speech2TextParaformer:
nbest: int = 1,
frontend_conf: dict = None,
hotword_list_or_file: str = None,
clas_scale: float = 1.0,
decoding_ind: int = 0,
**kwargs,
):
@ -382,6 +383,7 @@ class Speech2TextParaformer:
# 6. [Optional] Build hotword list from str, local file or url
self.hotword_list = None
self.hotword_list = self.generate_hotwords_list(hotword_list_or_file)
self.clas_scale = clas_scale
is_use_lm = lm_weight != 0.0 and lm_file is not None
if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
@ -446,16 +448,20 @@ class Speech2TextParaformer:
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
if not isinstance(self.asr_model, ContextualParaformer) and not isinstance(self.asr_model,
NeatContextualParaformer):
if not isinstance(self.asr_model, ContextualParaformer) and \
not isinstance(self.asr_model, NeatContextualParaformer):
if self.hotword_list:
logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
pre_token_length)
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
else:
decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds,
pre_token_length, hw_list=self.hotword_list)
decoder_outs = self.asr_model.cal_decoder_with_predictor(enc,
enc_len,
pre_acoustic_embeds,
pre_token_length,
hw_list=self.hotword_list,
clas_scale=self.clas_scale)
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
if isinstance(self.asr_model, BiCifParaformer):

2
funasr/bin/asr_inference_launch.py
View File

@ -260,6 +260,7 @@ def inference_paraformer(
export_mode = param_dict.get("export_mode", False)
else:
hotword_list_or_file = None
clas_scale = param_dict.get('clas_scale', 1.0)
if kwargs.get("device", None) == "cpu":
ngpu = 0
@ -292,6 +293,7 @@ def inference_paraformer(
penalty=penalty,
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
clas_sacle=clas_scale,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)

3
funasr/models/decoder/contextual_decoder.py
View File

@ -246,6 +246,7 @@ class ContextualParaformerDecoder(ParaformerSANMDecoder):
ys_in_pad: torch.Tensor,
ys_in_lens: torch.Tensor,
contextual_info: torch.Tensor,
clas_scale: float = 1.0,
return_hidden: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward decoder.
@ -285,7 +286,7 @@ class ContextualParaformerDecoder(ParaformerSANMDecoder):
cx, tgt_mask, _, _, _ = self.bias_decoder(x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask)
if self.bias_output is not None:
x = torch.cat([x_src_attn, cx], dim=2)
x = torch.cat([x_src_attn, cx*clas_scale], dim=2)
x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
x = x_self_attn + self.dropout(x)

4
funasr/models/e2e_asr_contextual_paraformer.py
View File

@ -343,7 +343,7 @@ class NeatContextualParaformer(Paraformer):
input_mask_expand_dim, 0)
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None):
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None, clas_scale=1.0):
if hw_list is None:
hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
hw_list_pad = pad_list(hw_list, 0)
@ -365,7 +365,7 @@ class NeatContextualParaformer(Paraformer):
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
decoder_outs = self.decoder(
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
)
decoder_out = decoder_outs[0]
decoder_out = torch.log_softmax(decoder_out, dim=-1)